Bornes PAC-Bayes et algorithmes d'apprentissage
|Advisor:||Marchand, Mario; Laviolette, François|
|Abstract:||The main purpose of this thesis is the theoretical study and the design of learning algorithms returning majority-vote classifiers. In particular, we present a PAC-Bayes theorem allowing us to bound the variance of the Gibbs’ loss (not only its expectation). We deduce from this theorem a bound on the risk of a majority vote tighter than the famous bound based on the Gibbs’ risk. We also present a theorem that allows to bound the risk associated with general loss functions. From this theorem, we design learning algorithms building weighted majority vote classifiers minimizing a bound on the risk associated with the following loss functions : linear, quadratic and exponential. Also, we present algorithms based on the randomized majority vote. Some of these algorithms compare favorably with AdaBoost.|
|Document Type:||Thèse de doctorat|
|Open Access Date:||16 April 2018|
|Collection:||Thèses et mémoires|
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